Syllable clustering analysis-based passive acoustic monitoring technology and its application in bird monitoring

Abstract

Aims: Passive acoustic monitoring has proven to be an effective method for monitoring bird biodiversity, as it allows for the analysis of important information such as bird songs and calls. The complexity and variations of bird songs and calls make it difficult to quickly and accurately identify bird species using voiceprint analysis. Solving this problem is essential for the successful implementation of a voiceprint-based bird diversity monitoring scheme. Methods: This paper proposes a syllable clustering analysis-based approach for bird song/call monitoring framework: The first step is to extract syllables from voiceprint data using audio features such as pitch and frequency flatness. These syllables are then trained using a combination of unsupervised representation learning and a Dirichlet process hybrid model. The final steps are clustering the syllables and inferring their categories. Results: (1) The analysis results show that, the proposed framework can achieve nearly 90% clustering accuracy when handling the published recordings of Lonchura striata song repository; (2) On the basis, the paper conducts unsupervised syllable clustering analysis on ten species of birds monitored in Baiyun Mountain Forest Park, Guangzhou, between April and May 2022. It verifies that the proposed framework can not only support bird species identification, but also meet the rapid species identification application requirements. This can be extended further to obtain the statistics and changes in time, frequency and quantity of various bird songs/calls. Conclusion: The analysis results of this paper show us that, the syllable clustering-based bird song/call monitoring framework can significantly reduce the requirements for manually annotated training data. This also overcomes the shortcomings of the traditional framework in dealing with overlapping bird songs. Therefore it provides a comprehensive solution for applications such as rapid species recognition, syllable sequence analysis, and population abundance analysis in bird diversity monitoring.